# Qdrant MCP tool for ADK

Supported in ADKPythonTypeScript

The [Qdrant MCP Server](https://github.com/qdrant/mcp-server-qdrant) connects your ADK agent to [Qdrant](https://qdrant.tech/), an open-source vector search engine. This integration gives your agent the ability to store and retrieve information using semantic search.

## Use cases

- **Semantic Memory for Agents**: Store conversation context, facts, or learned information that agents can retrieve later using natural language queries.
- **Code Repository Search**: Build a searchable index of code snippets, documentation, and implementation patterns that can be queried semantically.
- **Knowledge Base Retrieval**: Create a retrieval-augmented generation (RAG) system by storing documents and retrieving relevant context for responses.

## Prerequisites

- A running Qdrant instance. You can:
  - Use [Qdrant Cloud](https://cloud.qdrant.io/) (managed service)
  - Run locally with Docker: `docker run -p 6333:6333 qdrant/qdrant`
- (Optional) A Qdrant API key for authentication

## Use with agent

```python
from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import StdioConnectionParams
from mcp import StdioServerParameters

QDRANT_URL = "http://localhost:6333"  # Or your Qdrant Cloud URL
COLLECTION_NAME = "my_collection"
# QDRANT_API_KEY = "YOUR_QDRANT_API_KEY"

root_agent = Agent(
    model="gemini-flash-latest",
    name="qdrant_agent",
    instruction="Help users store and retrieve information using semantic search",
    tools=[
        McpToolset(
            connection_params=StdioConnectionParams(
                server_params=StdioServerParameters(
                    command="uvx",
                    args=["mcp-server-qdrant"],
                    env={
                        "QDRANT_URL": QDRANT_URL,
                        "COLLECTION_NAME": COLLECTION_NAME,
                        # "QDRANT_API_KEY": QDRANT_API_KEY,
                    }
                ),
                timeout=30,
            ),
        )
    ],
)
```

```typescript
import { LlmAgent, MCPToolset } from "@google/adk";

const QDRANT_URL = "http://localhost:6333"; // Or your Qdrant Cloud URL
const COLLECTION_NAME = "my_collection";
// const QDRANT_API_KEY = "YOUR_QDRANT_API_KEY";

const rootAgent = new LlmAgent({
    model: "gemini-flash-latest",
    name: "qdrant_agent",
    instruction: "Help users store and retrieve information using semantic search",
    tools: [
        new MCPToolset({
            type: "StdioConnectionParams",
            serverParams: {
                command: "uvx",
                args: ["mcp-server-qdrant"],
                env: {
                    QDRANT_URL: QDRANT_URL,
                    COLLECTION_NAME: COLLECTION_NAME,
                    // QDRANT_API_KEY: QDRANT_API_KEY,
                },
            },
        }),
    ],
});

export { rootAgent };
```

## Available tools

| Tool           | Description                                                    |
| -------------- | -------------------------------------------------------------- |
| `qdrant-store` | Store information in Qdrant with optional metadata             |
| `qdrant-find`  | Search for relevant information using natural language queries |

## Configuration

The Qdrant MCP server can be configured using environment variables:

| Variable                 | Description                                            | Default                                  |
| ------------------------ | ------------------------------------------------------ | ---------------------------------------- |
| `QDRANT_URL`             | URL of the Qdrant server                               | `None` (required)                        |
| `QDRANT_API_KEY`         | API key for Qdrant Cloud authentication                | `None`                                   |
| `COLLECTION_NAME`        | Name of the collection to use                          | `None`                                   |
| `QDRANT_LOCAL_PATH`      | Path for local persistent storage (alternative to URL) | `None`                                   |
| `EMBEDDING_MODEL`        | Embedding model to use                                 | `sentence-transformers/all-MiniLM-L6-v2` |
| `EMBEDDING_PROVIDER`     | Provider for embeddings (`fastembed` or `ollama`)      | `fastembed`                              |
| `TOOL_STORE_DESCRIPTION` | Custom description for the store tool                  | Default description                      |
| `TOOL_FIND_DESCRIPTION`  | Custom description for the find tool                   | Default description                      |

### Custom tool descriptions

You can customize the tool descriptions to guide the agent's behavior:

```python
env={
    "QDRANT_URL": "http://localhost:6333",
    "COLLECTION_NAME": "code-snippets",
    "TOOL_STORE_DESCRIPTION": "Store code snippets with descriptions. The 'information' parameter should contain a description of what the code does, while the actual code should be in 'metadata.code'.",
    "TOOL_FIND_DESCRIPTION": "Search for relevant code snippets using natural language. Describe the functionality you're looking for.",
}
```

## Additional resources

- [Qdrant MCP Server Repository](https://github.com/qdrant/mcp-server-qdrant)
- [Qdrant Documentation](https://qdrant.tech/documentation/)
- [Qdrant Cloud](https://cloud.qdrant.io/)